Abstract This study presents and evaluates a competency-based community-centered model for civil engineering and construction management education in which one first-year or sophomore student was paired with one senior student on a nonpaid course-related project experience supervised by municipal and/or private-sector engineering or construction professionals. Implemented at a Hispanic-Serving Institution, the model connects cross-level mentoring, authentic community-based projects, and bounded leadership opportunities for less-experienced students. Thirty-one paired teams completed 31 projects; the evaluated cohort included 62 students (31 first-year/sophomore students and 31 seniors; among the seniors, 21 were civil engineering majors and 10 were construction management majors). All participants completed an anonymous end-of-project survey aligned with the Accreditation Board for Engineering and Technology (ABET)-related outcomes and Bloom-informed higher-order learning activities. Descriptive results indicate moderate perceived gains across design/drafting, site/field exposure, learning and skill development, and role-specific leadership or mentoring outcomes. Open-ended responses were analyzed separately from Likert-type items and indicate that students valued exposure to authentic constraints, mentorship, teamwork, and professional communication. The results are translated into curricular implications for early professional exposure, structured cross-level mentoring, industry/community coordination, and explicit outcome mapping. The model offers a scalable approach for programs seeking to connect civil engineering and construction management curricula with authentic practice while supporting leadership development from the beginning of the undergraduate experience.
Abstract This study examined the relationship between personality, college adjustment, and student outcomes among undergraduate engineering students. Specifically, we investigated whether general personality traits based on the Big Five model, vocational personality types, and students’ adjustment to college predicted academic achievement and engineering identity. College adjustment was measured using the Student Adaptation to College Questionnaire (SACQ). Engineering identity was measured using engineering identity measure (EIM).Academic achievement was measured by cumulative grade point average (CGPA). We recruited 115 college students from diverse ethnic and cultural backgrounds, all of whom were enrolled in the civil engineering program at a private university in a metropolitan area in the US. Participants were classified into either above-average or below-average groups based on their CGPA and EIM scores. Five machine learning models were trained and evaluated using nested 10-fold cross-validation. Feature importance was examined using Shapley Additive exPlanations (SHAP) to identify the variables that contributed most strongly to academic achievement and engineering identity. The findings suggest that personality characteristics and college adjustment play important roles in college engineering students’ academic performance and engineering identity. Academic adjustment emerged as the strongest positive predictor of both CGPA and engineering identity, whereas neuroticism and extraversion were associated with lower academic achievement. Future researchers should examine factors affecting academic achievement, engineering identity, and adjustment to college among students from diverse backgrounds.
Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.
Abstract Augmented reality (AR) tools may support hydrology instruction by turning abstract watershed concepts into visible and manipulable processes. This pilot study reports the construction and classroom implementation of an AR sandbox to support basin and watershed understanding in an undergraduate civil engineering hydrology course in Bolivia. The system combines a depth sensor and projector with open-source software to overlay real-time elevation, contour lines, and simulated water on a physical sand surface. The AR sandbox was integrated into one laboratory session within a 20-week course, following a socio-constructivist approach in which students worked in small groups to iteratively model landforms and test hypotheses. The activity was organized around four learning objectives: (1) delineating watershed divides from terrain form; (2) identifying drainage direction and flow accumulation; (3) distinguishing endorheic basins, where water ponds internally, from exorheic basins, where water exits through an outlet; and (4) connecting topographic changes with runoff pathways through simulated rainfall. Student perceptions were collected through a brief postsession questionnaire and complemented by instructor observations. Self-reported responses indicated high engagement and perceived support for understanding key ideas such as watershed boundaries, drainage direction, basin outlets, and the contrast between closed and open hydrologic systems. From an instructional standpoint, instructor observations suggested that the hands-on visualization helped address common misconceptions and supported the transition to subsequent numerical and geographic information system (GIS)–based tasks. While the evidence is preliminary and based on short-term perception measures, the findings suggest that AR sandbox activities may serve as a practical and adaptable supplement to hydrology education, particularly in resource-constrained settings. Future implementations will incorporate performance-based assessment, including pre- and postactivity questions, rubric-based watershed-delineation tasks, and comparisons of student performance on drainage-network and GIS-based exercises.
Abstract Large language models (LLMs) are increasingly used by students and professionals in the architecture, engineering, and construction (AEC) sector for learning and information retrieval. However, their reliability in performing construction management (CM) knowledge tasks remains insufficiently characterized. This study introduces CMExamSet, a benchmark data set consisting of 689 multiple-choice questions compiled from four professional CM certification programs. The data set covers core CM domains, including project and program management, safety management, cost control, scheduling, contract administration, and related professional knowledge areas. Four contemporary LLMs were evaluated using a standardized zero-shot protocol with five repeated runs per question. Performance was assessed using accuracy, response consistency, subject-area analysis, and structured error annotation. Mean accuracy ranged from 79.2% to 90.0% across the four examinations, with high overall agreement observed in repeated runs. Performance varied systematically by domain: higher accuracy was observed in information retrieval-based domain tasks, such as engineering concepts, construction geomatics, and sustainability, whereas lower accuracy was observed in operationally oriented domains such as bidding and estimating, time and schedule management, and cost control. When errors occurred, conceptual misunderstandings were the most frequently observed error type across domains, indicating that incorrect interpretation or application of domain principles remained a common source of failure. Performance improvements in newer models were generally modest across subject areas. These findings provide empirical evidence on the capabilities and limitations of LLMs in CM knowledge assessment and underscore the importance of structured verification, domain grounding, and instructional guidance when integrating LLM tools into construction education and professional preparation.
